Inspired by video-based object segmentation methods [1]}, [2]}, [3]} using optical flow to obtain appearance features, we add a spatial and channel attention module [1]} to extract motion information from residual images. Such motion information enhances the appearance features extracted from range images, i.e., exploiting motion information and emphasizing more salient areas in appearance features.
As illustrated in Fig. REF , we use a similar structure to Li et al.  [1]} to fuse the features of the two branches.
We denote \(f_a\)  as the appearance feature of range images from Enc-A and \(f_m\)  as the motion feature of residual images from Enc-M branch, and have:
\(f_{a}^{\prime } &\!=\!f_{a} \otimes \operatorname{Sigmoid}\left(\operatorname{Conv_{1\times 1}}\left(f_{m}\right)\right), \\f_{a}^{\prime \prime } &\!=\!f_{a}^{\prime } \otimes \left[\operatorname{Softmax}\left(\operatorname{Conv_{1\times 1}}\left(\operatorname{APool}\left(f_{a}^{\prime }\right)\right)\right) \cdot C\right]+f_{a},\) 
